Details

Autor(en) / Beteiligte

• Otaru, Abdulrazak Jinadu
• Auta, Manase

Titel

Machine learning backpropagation network analysis of permeability, Forchheimer coefficient, and effective thermal conductivity of macroporous foam–fluid systems

Ist Teil von

• International journal of thermal sciences, 2024-07, Vol.201, p.109039, Article 109039

Ort / Verlag

Elsevier Masson SAS

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Macroporous materials exhibit outstanding properties in heat and mass transfer due to their high pore volume, high surface area, and high Young's modulus. Consequently, understanding their thermofluidic properties is crucial in the design, synthesis, and optimal application of these materials. Therefore, this study, premieres, the use of a machine learning (ML) backpropagation network to develop and train a series of datasets for permeability, Forchheimer coefficient, and effective thermal conductivity of variable macroporous foam–fluid systems with respect to degrees of interstices, fluid and solid properties. To account for permeability values for flowing fluids in the Darcy regime, numerical simulations of slow–moving fluids were implemented over the materials' interstices. In comparison to similarly substantiated values of permeability in the Forchheimer regime, these values were a bit lower. The ML-based backpropagation algorithm was used to analyze data, which produced predictions (output signals) that are more than 90 % in correlation to CFD datasets. This provided insight into the effect of porosity and reduced mean pore openings on macroporous structures' thermofluidic behaviour. Material porosity was observed to play a dominant role in estimating Forchheimer coefficients and effective thermal conductivities for these foam-fluid systems. However, reduced mean pore openings were observed to be more critical for estimating permeability. The contributory effects of reduced mean pore openings on the effective thermal conductivity for these macroporous foam–fluid systems were determined to vary between 5.8 and 13.2 percent. Furthermore, the effective thermal conductivity of macroporous foam–fluid systems was also evaluated in relation to changes in the interstitial fluid and solid matrix thermal conductivity. •Machine learning (ML) backpropagation network is used in this study to develop a set of datasets for permeability, Forchheimer coefficient, and effective thermal conductivity of macroporous foams and fluids.•Simulations of slow-moving fluid over the interstices of these materials were performed to account for the permeability values of flowing fluids in the Darcy regime.•According to ML-based DNN, these materials' thermofluidic performance was greatly influenced by their reduced porosity and mean pore openings.•Forchheimer coefficients and effective thermal conductivities of foam-fluid systems were significantly affected by porosity, but reduced mean pore openings were more important when estimating permeability.•Reduced mean pore openings contributed between 5.8 and 13.2 percent to the effective thermal conductivity of these foam-fluid systems.